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| 001 | 1022040 | ||
| 005 | 20240202202648.0 | ||
| 024 | 7 | _ | |a 10.1101/2024.01.12.574450 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-01177 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-01177 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |a Jung, Kyesam |0 P:(DE-Juel1)178611 |b 0 |e First author |
| 245 | _ | _ | |a Simulated brain networks reflecting progression of Parkinson’s disease |
| 260 | _ | _ | |a Cold Spring Harbor |c 2024 |b Cold Spring Harbor Laboratory, NY |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1706854612_29240 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a Neurodegenerative progression of Parkinson’s disease affects brain structure and function and, concomitantly, alters topological properties of brain networks. The network alteration accompanied with motor impairment and duration of the disease is not yet clearly demonstrated in the disease progression. In this study, we aim at resolving this problem with a modeling approach based on large-scale brain networks from cross-sectional MRI data. Optimizing whole-brain simulation models allows us to discover brain networks showing unexplored relationships with clinical variables. We observe that simulated brain networks exhibit significant differences between healthy controls (n=51) and patients with Parkinson’s disease (n=60) and strongly correlate with disease severity and disease duration of the patients. Moreover, the modeling results outperform the empirical brain networks in these clinical measures. Consequently, this study demonstrates that utilizing simulated brain networks provides an enhanced view on network alterations in the progression of motor impairment and potential biomarkers for clinical indices. |
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| 700 | 1 | _ | |a Popovych, Oleksandr |0 P:(DE-Juel1)131880 |b 3 |e Corresponding author |
| 773 | _ | _ | |a 10.1101/2024.01.12.574450 |0 PERI:(DE-600)2766415-6 |t bioRxiv beta |y 2024 |
| 856 | 4 | _ | |u https://www.biorxiv.org/content/10.1101/2024.01.12.574450v1 |
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